#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
太极动力学核心模块
实现宇宙阴阳动态平衡的演化过程
"""

import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import numpy as np
from typing import Tuple, Optional

class CosmicTaijiDynamics(nn.Layer):
    """
    宇宙太极动力学
    实现'道生一，一生二，二生三，三生万物'的演化过程
    """
    
    def __init__(self, 
                 cosmic_dim: int = 1024,
                 num_iterations: int = 3):
        """
        初始化太极动力学
        
        Args:
            cosmic_dim: 宇宙维度
            num_iterations: 演化迭代次数（对应三生万物）
        """
        super().__init__()
        
        self.cosmic_dim = cosmic_dim
        self.num_iterations = num_iterations
        
        # 阳动力网络：创造、生发、显现
        self.yang_dynamics = nn.Sequential(
            nn.Linear(cosmic_dim, cosmic_dim * 2),
            nn.Tanh(),  # 双曲正切象征创造性爆发
            nn.Linear(cosmic_dim * 2, cosmic_dim),
            nn.ReLU()   # ReLU象征积极展现
        )
        
        # 阴动力网络：回归、收敛、潜藏
        self.yin_dynamics = nn.Sequential(
            nn.Linear(cosmic_dim, cosmic_dim * 2),
            nn.Sigmoid(),  # Sigmoid象征收敛性
            nn.Linear(cosmic_dim * 2, cosmic_dim),
            nn.LeakyReLU(0.1)  # LeakyReLU象征深层潜力
        )
        
        # 鱼眼机制：动态转换通道
        self.fish_eye_gates = nn.LayerList([
            nn.Linear(cosmic_dim * 2, cosmic_dim) 
            for _ in range(num_iterations)
        ])
        
        # 宇宙常数（调节阴阳平衡）
        self.cosmic_constants = self.create_parameter(
            shape=[num_iterations, cosmic_dim],
            default_initializer=nn.initializer.Normal(std=0.02)
        )
        
        # 阴阳平衡监测器
        self.balance_monitor = BalanceMonitor(cosmic_dim)
    
    def forward(self, x: paddle.Tensor, iteration: int) -> paddle.Tensor:
        """
        太极动力学演化
        
        Args:
            x: 输入状态
            iteration: 当前演化阶段 (0,1,2)
            
        Returns:
            演化后的状态
        """
        # 获取当前阶段的宇宙常数
        cosmic_constant = self.cosmic_constants[iteration]
        
        # 同步计算阴阳动力
        yang_force = self.yang_dynamics(x)  # 创造性能量流
        yin_force = self.yin_dynamics(x)    # 回归性能量流
        
        # 鱼眼机制：动态平衡
        combined = paddle.concat([yang_force, yin_force], axis=1)
        balance_gate = F.sigmoid(self.fish_eye_gates[iteration](combined))
        
        # 阴阳调和（核心公式）
        # balanced = gate * yang + (1-gate) * yin + cosmic_constant
        balanced_output = (
            balance_gate * yang_force + 
            (1 - balance_gate) * yin_force + 
            cosmic_constant
        )
        
        # 宇宙归一化
        output_norm = paddle.norm(balanced_output, p=2, axis=1, keepdim=True)
        normalized_output = balanced_output / (output_norm + 1e-8)
        
        # 记录平衡状态
        self.balance_monitor.record(
            iteration=iteration,
            yang_force=yang_force,
            yin_force=yin_force,
            balance_gate=balance_gate,
            final_output=normalized_output
        )
        
        return normalized_output
    
    def get_balance_report(self) -> dict:
        """获取阴阳平衡报告"""
        return self.balance_monitor.get_report()

class BalanceMonitor:
    """阴阳平衡监测器"""
    
    def __init__(self, cosmic_dim: int):
        self.cosmic_dim = cosmic_dim
        self.records = []
    
    def record(self, iteration: int, yang_force: paddle.Tensor, 
               yin_force: paddle.Tensor, balance_gate: paddle.Tensor,
               final_output: paddle.Tensor):
        """记录平衡状态"""
        record = {
            'iteration': iteration,
            'yang_norm': paddle.norm(yang_force).item(),
            'yin_norm': paddle.norm(yin_force).item(),
            'balance_gate_mean': paddle.mean(balance_gate).item(),
            'output_stability': paddle.std(final_output).item(),
            'yang_yin_ratio': (paddle.norm(yang_force) / 
                              (paddle.norm(yin_force) + 1e-8)).item()
        }
        self.records.append(record)
    
    def get_report(self) -> dict:
        """生成平衡报告"""
        if not self.records:
            return {}
        
        latest = self.records[-1]
        yang_yin_ratio = latest['yang_yin_ratio']
        
        # 平衡状态评估
        if 0.9 <= yang_yin_ratio <= 1.1:
            balance_status = "完美平衡"
            balance_score = 1.0
        elif 0.7 <= yang_yin_ratio <= 1.3:
            balance_status = "良好平衡" 
            balance_score = 0.8
        elif 0.5 <= yang_yin_ratio <= 1.5:
            balance_status = "基本平衡"
            balance_score = 0.6
        else:
            balance_status = "失衡状态"
            balance_score = 0.3
        
        return {
            'balance_status': balance_status,
            'balance_score': balance_score,
            'yang_yin_ratio': yang_yin_ratio,
            'current_iteration': latest['iteration'],
            'stability': latest['output_stability'],
            'all_records': self.records
        }

class QuantumTaijiDynamics(nn.Layer):
    """
    量子太极动力学
    结合量子计算概念的太极演化
    """
    
    def __init__(self, cosmic_dim: int = 1024):
        super().__init__()
        self.cosmic_dim = cosmic_dim
        
        # 量子风格的阴阳算子
        try:
            # 尝试使用Paddle Quantum（如果可用）
            import paddle_quantum as pq
            self.use_quantum = True
            
            self.yang_circuit = self._create_quantum_circuit('yang')
            self.yin_circuit = self._create_quantum_circuit('yin')
            
        except ImportError:
            self.use_quantum = False
            # 经典退化解
            self.yang_circuit = nn.Linear(cosmic_dim, cosmic_dim)
            self.yin_circuit = nn.Linear(cosmic_dim, cosmic_dim)
        
        # 量子纠缠融合层
        self.entanglement_fusion = EntanglementFusion(cosmic_dim)
    
    def _create_quantum_circuit(self, circuit_type: str) -> nn.Layer:
        """创建量子电路"""
        # 简化的量子电路模拟
        # 实际应用中应该使用真实的量子电路
        if circuit_type == 'yang':
            return nn.Sequential(
                nn.Linear(self.cosmic_dim, self.cosmic_dim * 2),
                nn.Tanh(),
                nn.Linear(self.cosmic_dim * 2, self.cosmic_dim)
            )
        else:  # yin
            return nn.Sequential(
                nn.Linear(self.cosmic_dim, self.cosmic_dim * 2),
                nn.Sigmoid(),
                nn.Linear(self.cosmic_dim * 2, self.cosmic_dim)
            )
    
    def forward(self, x: paddle.Tensor, iteration: int) -> paddle.Tensor:
        """量子太极演化"""
        # 量子阴阳计算
        if self.use_quantum:
            yang_quantum = self.yang_circuit(x)
            yin_quantum = self.yin_circuit(x)
        else:
            # 经典计算退化解
            yang_quantum = self.yang_circuit(x)
            yin_quantum = self.yin_circuit(x)
        
        # 量子纠缠融合
        entangled_state = self.entanglement_fusion(yang_quantum, yin_quantum)
        
        # 根据演化阶段调整
        evolution_factor = (iteration + 1) / self.cosmic_dim
        evolved_state = entangled_state * evolution_factor
        
        return F.normalize(evolved_state)

class EntanglementFusion(nn.Layer):
    """量子纠缠融合层"""
    
    def __init__(self, cosmic_dim: int):
        super().__init__()
        self.cosmic_dim = cosmic_dim
        
        # 纠缠权重矩阵
        self.entanglement_matrix = self.create_parameter(
            shape=[cosmic_dim, cosmic_dim],
            default_initializer=nn.initializer.Orthogonal()
        )
    
    def forward(self, state1: paddle.Tensor, state2: paddle.Tensor) -> paddle.Tensor:
        """量子纠缠融合"""
        # 张量积创建纠缠态
        entangled = paddle.outer(state1, state2)
        
        # 应用纠缠矩阵
        entangled = paddle.matmul(entangled, self.entanglement_matrix)
        
        # 全息压缩回原维度
        compressed = self.holographic_compression(entangled, state1.shape)
        
        return compressed
    
    def holographic_compression(self, large_state: paddle.Tensor, 
                              target_shape: Tuple) -> paddle.Tensor:
        """全息压缩"""
        # 使用SVD进行信息压缩
        matrix_form = large_state.reshape([-1, large_state.shape[-1]])
        U, s, Vh = paddle.linalg.svd(matrix_form, full_matrices=False)
        
        # 保留主要成分（全息原理）
        k = min(target_shape[-1], len(s))
        compressed = paddle.matmul(U[:, :k], paddle.diag(s[:k]))
        
        return compressed.reshape(target_shape)